Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology.

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Abstract

The advancement of high-throughput omics technologies is leading plant biology research into the era of Big Data. Machine learning (ML) performs an important role in plant systems biology owing to its excellent performance and wide application in Big Data analysis. However, supervised ML algorithms require large numbers of labeled samples as training data to achieve ideal performance. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, unsupervised learning (UL) and semi-supervised learning (SSL) paradigms play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significances and challenges of UL and SSL strategies in plant systems biology.This article is protected by copyright. All rights reserved.